Publications
その他 (国内) Predicting Urban Dynamics with GPS data by Multi-Order Poisson Regression Model
Yanru Chen (Tokyo Tech) Hayakawa Yuta(Tokyo Tech), Tsubouchi Kota, Masamichi Shimosaka(Tokyo Tech)
第62回情報処理学会 ユビキタスコンピューティングシステム研究会 (IPSJ SIGUBI)
2019.6.6
Forecasting people flow in urban regions (urban dynamics) is playing an increasingly important role in urban planning, emergency management, public services, and commercial activities. In this paper, we propose a Multi-Order Poisson Regression Model for urban dynamics prediction based on an enriched and generalized feature representation. In the proposed method, new features are produced by employing a variety of polynomial combinations of multiple factors which greatly affect people flow (e.g., time-of-the-day, day-of-the-week, weather situation, holiday information). The results obtained from an experiment with a massive GPS dataset show that the proposed method is capable of producing models which have higher prediction accuracy compared to the state-of-the-art method.
Paper : Predicting Urban Dynamics with GPS data by Multi-Order Poisson Regression Model （外部サイト）